If your expectations were for Elon Musk-worrying AI, dial them back to the level of business software. It’s actually less mundane than it sounds. AI already has changed how we use data to make sense of our world. It’s also become a corporate fashion. For every AlphaGo Zero there are a thousand firms — startups and established companies — sticking the AI label on their wares like pinstripes on 1980s cars.

I don’t doubt that the last few years have seen significant and rapid progress in AI. What I mistrust is the crescendo of hype, which echoes the tech bubble of the late 1990s and early 2000s. The risk: buying into over-exuberant promises rather than products with a proven return on investment (ROI). Hype clouds our judgment, sometimes intentionally.

If you share my skepticism, but likewise sense an important opportunity and want to avoid excessive caution, here are six questions to help you tune your BS detector.

1. What business problem am I trying to solve?

This is the most important question, and it has nothing whatsoever to do with AI. Granted, a few firms will find value in experimentation, but open-ended projects should be treated with extreme caution. Better to clearly define the business problem you want to solve.

You should evaluate any business investment against three criteria: Will it increase revenue, reduce costs or mitigate risk? Anchoring new technology to at least one of these fundamentals will establish its value. After that, assigning ownership and accountability is the best way to keep a technology initiative on track.

2. Why do I need AI to solve this problem?

Maybe you don’t. True AI acquires and applies knowledge and skills. It’s good for situations in which variability and novelty exist, but it’s difficult to build and therefore commands a premium. Consider the complexity of a self-driving car navigating busy city streets. Does your business problem involve continual unpredictability?

Machines that get incrementally better at a task sound compelling, but you need to focus on the outcomes delivered and not the technology used to achieve them. Will you gain a margin of improvement that makes the cost of AI worthwhile? Figure out a test to evaluate the size of the margin. Run it on paper and again in a proof-of-concept project. Make sure AI earns its premium.

3. Do I have sufficient data to use AI?

The best AI solutions outperform people at specific tasks such as recognizing cancer cells in scans or finding errant traders in an investment bank. But teaching a machine to make sense of messy and inconsistent data requires extensive training. AI uses models to make sense of the world and generalize. Finding enough examples to build a good model can be difficult.

Healthcare systems or banks can draw on extensive historic data. Can you? Even if you can, trawling through it to find relevant examples can be time-consuming and costly. To overcome this obstacle, some companies have started to work on AI model-training software that makes the process quicker and cheaper. Even so, it’s a tough assignment whenever data is scarce.

4. Should I build or buy an AI solution?

If you’re trying to embed AI into your own products or services, an in-house capability might make sense. Still, don’t underestimate the resources and specialized expertise involved. A carefully selected partner may offer a quicker path to glory.

If you’re tackling a known business problem, you’ll likely be better off working with an experienced vendor. Don’t be blinded by technology; whatever you buy will need to be customized or adapted to your environment and requirements. Focus on your vendor’s understanding of your business domain and the type of data the consultant will need to leverage.

5. How well does the vendor know my domain?

Some vendors claim AI makes domain experience irrelevant. Don’t believe it. It’s quicker and less stressful to work with a consultant who doesn’t need to learn your business from scratch.

Check any potential vendor’s relevant experience and partnerships. Can the vendor’s leaders give production examples of comparable problems solved for others? If your problem truly is unique, seek experts who bring experience in dealing with parallel challenges — perhaps in a different industry with similar sorts of data.

6. Is there a proven ROI?

In Gartner’s report, the “peak of inflated expectations” is followed by the similarly whimsical “trough of disillusionment.” AI will lose its luster as technology buyers look past blithe promises and start demanding proven results.

In my opinion, that’s merely sensible business practice. So why be patient? Ask to see ROI measures today. Your smart money is on solutions that don’t involve an extended learning curve for the buyer or the vendor.

While I’m obviously a bit of a cynic, I’ve seen firsthand the difference that genuine AI can make. Machines can take on tasks that are important but arduous for people — and do them better. It does more than save time or give a nudge to performance. It opens space for a shift in organizational change that reaps far greater rewards. That’s the true promise of AI, but realizing it takes more than clever software.

Amara’s law states that “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It’s sound advice. Leaders face the twin challenge of deciding if an investment in AI can create value in the near term while figuring out how to adapt their organizations for a world in which AI is ubiquitous.

Fostering positive experiences and making investments that deliver a healthy ROI will help reveal what’s at stake and lay the foundation for deeper change ahead. Heeding business fundamentals rather than hype will allow organizations to make smart choices today and build their experience of AI for the future.